迅速な災害管理のための即時的,持続可能，かつ拡張的なエッジコンピューティングの研究

本学位論文は、迅速な災害管理におけるいくつかの問題に取り組んだ。既存のネットワークインフラが災害による直接的なダメージや停電によって使えないことを想定し、本論文では、最新のICTを用いた次世代災害支援システムの構築を目指す。以下のとおり本論文は三部で構成される。第一部は、災害発生後の緊急ネットワーキングである。本論文では、情報指向フォグコンピューティング（Information-Centric Fog Computing）というアーキテクチャを提案し、既存のインフラがダウンした場合に臨時的なネットワーク接続を提供する。本論文では、六次の隔たり理論から着想を得て、緊急時向け名前ベースルーティング（Name-Based Routing）を考慮した。まず、二層の情報指向フォグコンピューティングネットワークモデルを提案した。次に、ソーシャルネットワークを元に、情報指向フォグノード間の関係をモデリングし、名前ベースルーティングプロトコルをデザインする。シミュレーション実験では、既存のソリューションと比較し、提案手法はより高い性能を示し、有用性が証明された。第二部は、ネットワークの通信効率の最適化である。本論文は、第一部で構築されたネットワークの通信効率を最適化し、ネットワークの持続時間を延ばすために、ネットワークのエッジで行われるキャッシングストラテジーを提案した。本論文では、まず、第一部で提案した二層ネットワークモデルをベースにサーバー層も加えて、異種ネットワークストラクチャーを構成した。次に、緊急時向けのエッジキャッシングに必要なTime to Live （TTL）とキャッシュ置換ポリシーを設計する。シミュレーション実験では、エネルギー消費とバックホールレートを性能指標とし、メモリ内キャッシュとディスクキャッシュの性能を比較した。結果では、メモリ内ストレージと処理がエッジキャッシングのエネルギーを節約し、かなりのワークロードを共有できることが示された。第三部は、ネットワークカバレッジの拡大である。本論文は、ドローンの関連技術とリアルタイム視覚認識技術を利用し、被災地のユーザ捜索とドローンの空中ナビゲーションを行う。災害管理におけるドローン制御に関する研究を調査し、現在のドローン技術と無人捜索救助に対する実際のニーズを考慮すると、軽量なソリューションが緊急時に必要であることが判明した。そのため本論文では、転移学習を利用し、ドローンに搭載されたオンボードコンピュータで実行可能な空中ビジョンに基づいたナビゲーションアプローチを開発した。シミュレーション実験では、1/150ミニチュアモデルを用いて、空中ナビゲーションの実行可能性をテストした。結果では、本論文で提案するドローンの軽量ナビゲーションはフィードバックに基づいてリアルタイムに飛行の微調整を実現でき、既存手法と比較して性能において大きな進歩を示した。This dissertation mainly focuses on solving the problems in agile disaster management. To face the situation when the original network infrastructure no longer works because of disaster damage or power outage, I come up with the idea of introducing different emerging technologies in building a next-generation disaster response system. There are three parts of my research. In the first part of emergency networking, I design an information-centric fog computing architecture to fast build a temporary emergency network while the original ones can not be used. I focus on solving name-based routing for disaster relief by applying the idea from six degrees of separation theory. I first put forward a 2-tier information-centric fog network architecture under the scenario of post-disaster. Then I model the relationships among ICN nodes based on delivered files and propose a name-based routing strategy to enable fast networking and emergency communication. I compare with DNRP under the same experimental settings and prove that my strategy can achieve higher work performance. In the second part of efficiency optimization, I introduce the idea of edge caching in prolong the lifetime of the rebuilt network. I focus on how to improve the energy efficiency of edge caching using in-memory storage and processing. Here I build a 3-tier heterogeneous network structure and propose two edge caching methods using different TTL designs & cache replacement policies. I use total energy consumption and backhaul rate as the two metrics to test the performance of the in-memory caching method and compare it with the conventional method based on disk storage. The simulation results show that in-memory storage and processing can help save more energy in edge caching and share a considerable workload in percentage. In the third part of coverage expansion, I apply UAV technology and real-time image recognition in user search and autonomous navigation. I focus on the problem of designing a navigation strategy based on the airborne vision for UAV disaster relief. After the survey of related works on UAV fly control in disaster management, I find that in consideration of the current UAV manufacturing technology and actual demand on unmanned search & rescue, a lightweight solution is in urgent need. As a result, I design a lightweight navigation strategy based on visual recognition using transfer learning. In the simulation, I evaluate my solutions using 1/150 miniature models and test the feasibility of the navigation strategy. The results show that my design on visual recognition has the potential for a breakthrough in performance and the idea of UAV lightweight navigation can realize real-time flight adjustment based on feedback.室蘭工業大学 (Muroran Institute of Technology)博士（工学

Using multiclass classification algorithms to improve text categorization tool:NLoN

Abstract. Natural language processing (NLP) and machine learning techniques have been widely utilized in the mining software repositories (MSR) field in recent years. Separating natural language from source code is a pre-processing step that is needed in both NLP and the MSR domain for better data quality. This paper presents the design and implementation of a multi-class classification approach that is based on the existing open-source R package Natural Language or Not (NLoN). This article also reviews the existing literature on MSR and NLP. The review classified the information sources and approaches of MSR in detail, and also focused on the text representation and classification tasks of NLP. In addition, the design and implementation methods of the original paper are briefly introduced. Regarding the research methodology, since the research goal is technology-oriented, i.e., to improve the design and implementation of existing technologies, this article adopts the design science research methodology and also describes how the methodology was adopted. This research implements an open-source Python library, namely NLoN-PY. This is an open-source library hosted on GitHub, and users can also directly use the tools published to the PyPI library. Since NLoN has achieved comparable performance on two-class classification tasks with the Lasso regression model, this study evaluated other multi-class classification algorithms, i.e., Naive Bayes, k-Nearest Neighbours, and Support Vector Machine. Using 10-fold cross-validation, the expanded classifier achieved AUC performance of 0.901 for the 5-class classification task and the AUC performance of 0.92 for the 2-class task. Although the design of this study did not show a significant performance improvement compared to the original design, the impact of unbalanced data distribution on performance was detected and the category of the classification problem was also refined in the process. These findings on the multi-class classification design can provide a research foundation or direction for future research

Thermal Responsive Shape Memory Polymers for Biomedical Applications

Summary: Shape memory polymers (SMPs) are a type of polymeric materials that can be programmed to memorize a less-constrained shape/configuration, subsequently assume a strained temporary shape/configuration, and then revert to the memorized shape/configuration upon triggering by an external stimulus(Lendlein & Kelch, 2002). Such responsiveness to stimuli is reminiscent of the adaptive responses universally observed in living organisms. Based on the nature of the external stimulus, SMPs can be categorized into light-responsive SMPs, chemical-responsive SMPs, magnetic field-responsive SMPs, and thermal-responsive SMPs, etc. Thermal-responsive SMPs are one of the most studied systems and will be the focus of this chapter

Preliminary test almost unbiased ridge estimator in a linear regression model with multivariate Student-t errors

In this paper, the preliminary test almost unbiased ridge estimators of the regression coefficients based on the conflicting Wald (W), Likelihood ratio (LR) and Lagrangian multiplier (LM) tests in a multiple regression model with multivariate Student-t errors are introduced when it is suspected that the regression coefficients may be restricted to a subspace. The bias and quadratic risks of the proposed estimators are derived and compared. Sufficient conditions on the departure parameter ∆ and the ridge parameter k are derived for the proposed estimators to be superior to the almost unbiased ridge estimator, restricted almost unbiased ridge estimator and preliminary test estimator. Furthermore, some graphical results are provided to illustrate theoretical results

A Causal And-Or Graph Model for Visibility Fluent Reasoning in Tracking Interacting Objects

Tracking humans that are interacting with the other subjects or environment remains unsolved in visual tracking, because the visibility of the human of interests in videos is unknown and might vary over time. In particular, it is still difficult for state-of-the-art human trackers to recover complete human trajectories in crowded scenes with frequent human interactions. In this work, we consider the visibility status of a subject as a fluent variable, whose change is mostly attributed to the subject's interaction with the surrounding, e.g., crossing behind another object, entering a building, or getting into a vehicle, etc. We introduce a Causal And-Or Graph (C-AOG) to represent the causal-effect relations between an object's visibility fluent and its activities, and develop a probabilistic graph model to jointly reason the visibility fluent change (e.g., from visible to invisible) and track humans in videos. We formulate this joint task as an iterative search of a feasible causal graph structure that enables fast search algorithm, e.g., dynamic programming method. We apply the proposed method on challenging video sequences to evaluate its capabilities of estimating visibility fluent changes of subjects and tracking subjects of interests over time. Results with comparisons demonstrate that our method outperforms the alternative trackers and can recover complete trajectories of humans in complicated scenarios with frequent human interactions.Comment: accepted by CVPR 201

Study on transient voltage distribution characteristics of transformer windings

Very fast transient overvoltage (VFTO) with large waveform steepness and high amplitude can cause great harm to winding insulation of transformers and other equipment connected to it. Therefore, a transformer winding model is designed and described in this paper. Transient overvoltage test is carried out by pinning method and non-contact measuring sensor respectively. Then the simulation results are compared with those of VFTO distribution calculation program for transformer equipment. The results show that the simulation results are close to the actual test results. Finally, combining with the experimental measurement and simulation data, the potential distribution in transformer windings under the effect of fast transient overvoltage is analysed to guide the overvoltage protection of transformer winding insulation

Real-Time Awareness Scheduling for Multimedia Big Data Oriented In-Memory Computing

As one of the most striking research hotspots in both academia and industry, Internet of Things (IoT) has been constantly changing our daily life by joining together nearly all we can imagine. From home furnishings and vehicles to urban facilities, all these smart things need powerful managing and processing capabilities to deal with mass multimedia data in different content forms such as images, audios, and videos. Nowadays, since Moore\u27s Law is no longer applicable, conventional thinking may not be adequate in facing the explosive growing amount of data. Hence, in this paper, we adopt the idea of in-memory processing to solve the problem of real-time multimedia big data computing in IoT. We apply closed-loop feedback in the scheduling method design to integrate in-memory storages of all devices within a 3-tier network structure. In addition, we consider the respective conditions of different real-time required levels and content forms. The analysis results show that our scheduling method can achieve better workload allocation with less latency in comparison of existing methods

Saving Energy on Edge: In-Memory Caching for Multi-Tier Heterogeneous Network

Recent years have witnessed billions of new manufactured sensors, equipments, and machines being connected to our almost omnipotent Internet. While enjoying the comfort and convenience brought by IoT, we also have to face tremendous energy consumption and carbon emissions that even cause climate deterioration. Extended from cloud computing, edge/fog computing and caching provide new thoughts on processing big data generated from distributed IoT devices. With the purpose of helping deal with the data explosion problem by edge caching, in this article we apply in-memory storage and processing to reduce energy consumption. We design two kinds of TTL in four cache replacement policies to cache data at the edge. We carry out a simulation experiment in a three-tier heterogeneous network structure using the RWP model and test the performance of in-memory caching and the traditional method. The analysis results manifest that our in-memory method is able to obtain better energy efficiency in edge caching, and has stable and low backhaul rate